Internal variability of a dynamically downscaled climate over North America

Wang, Jiali; Bessac, Julie; Kotamarthi, Rao; Constantinescu, Emil M.; Drewniak, Beth

doi:10.1007/s00382-017-3889-1

Cited by 6 publications

(10 citation statements)

References 23 publications

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“…A model’s internal variability could be large due to the nonlinearity of the WRF model when the model simulation is at a higher spatial resolution. For example, the internal variability for a simulation with 12‐km resolution is twice as large as that for a simulation with 50‐km resolution in the summer over the central and eastern United States (Wang et al., 2018). The internal variability is especially important during the summer over the GLR, because part of the summer precipitation is driven by local convective processes that are much more prone to triggering internal variability within the RCM.…”

Section: Model Data and Approachmentioning

confidence: 99%

Impacts of Lake Surface Temperature on the Summer Climate Over the Great Lakes Region

et al. 2022

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The total surface area of the Great Lakes is over 94,250 miles 2 . Consisting of five large lakes, they contain over one fifth of the world's total surface fresh water, with depths of up to 400 m (EPA, 2020). The Great Lakes substantially influence the regional weather and climate via their effects on the atmospheric energy and water budget; open water typically has greater thermal conductance, lower albedo, and lower roughness compared to soil or vegetated surfaces (Notaro et al.,2013;Scott & Huff, 1996). The large thermal inertia of the lakes leads to a reduction in annual and diurnal temperature ranges across the Great Lakes Basin (Harris & Kotamarthi, 2005;Notaro et al., 2013). Mean minimum temperatures in the region are warmer during all seasons and over all five lakes, while maximum temperatures are cooler during spring and summer (Scott & Huff, 1996). Lake thermal impacts and associated evaporation have a strong influence on regional precipitation patterns. While many studies have investigated the impact of lake surface temperature (LST) on the cold-season precipitationwhich is primarily a direct product of warmth and moisture from the Great Lakes (e.g., lake-effect snow; Notaro et al., 2021; Shi & Xue, 2019)-the impact of LST on summer precipitation has only rarely been studied. During early summer, the lake surface is still relatively cool compared to the atmosphere, resulting in condensation on the lake surface and a stable boundary layer that deters over-lake convective processes (Miner & Fritsch, 1997;Workoff et al., 2012). Due to the temperature differences over lake and inland, lake breeze can cause air to rise inland and increases the likelihood of over-land precipitation (Schulkowski, 2020). During late summer, while the land cools and the lakes remain warm, the temperature differential between land and lake surface coupled with baroclinic waves (e.g., cold front and trough) and/or the Great Plains low-level jets can generate enhanced precipitation (Feng et al., 2016;Miner & Fritsch, 1997). Therefore, changes in summer LST could potentially affect its feedback to atmospheric stability and change the water and energy budget over the entire Great Lakes Region (GLR). The summer is an ideal time to evaluate the influence of LST on the lake surface-atmosphere

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Section: Model Data and Approachmentioning

confidence: 99%

Impacts of Lake Surface Temperature on the Summer Climate Over the Great Lakes Region

et al. 2022

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“…These ensemble members use exactly the same model configuration such as physics parameterizations, spatial resolutions and nudging techniques, except that their initial conditions are slightly different. Further details about the experiment design can be found in Wang et al (2018). Previous studies found that the IV was neither affected by the time period (i.e., past versus future) nor by the type of driving data (i.e., reanalysis data versus GCM output, Braun et al, 2012).…”

Section: Regional Climate Model Outputsmentioning

confidence: 99%

“…In this section, we propose new statistics based on the commonly used internal variability (IV). IV arises from intrinsic variations of the nonlinear physical and dynamical processes that are described by climate models (Hawkins and Sutton, 2009;Wang et al, 2018). Due to the restrictions on the large-scale atmospheric flow imposed by the lateral boundary conditions, the level of IV generated by RCMs is smaller than those generated by GCMs (at least at the large scale).…”

Section: Internal Variability and Projected Climate Change Signalmentioning

confidence: 99%

“…where Q n (i, j, T ) is the 95th-quantile of the ensembles over a season T . When projected climate changes (PCC), which are derived as the difference between a historical and projected statistics, are two-fold larger than the internal variability, changes can be considered robust with respect to the IV (Wang et al, 2018). These computations are performed on WRF-CCSM ensemble described in Sect.…”

Section: Internal Variability and Projected Climate Change Signalmentioning

confidence: 99%

“…From previous discussions we noticed that several locations may show decrease in wind speed at their dominant wind directions, while other locations may experience changes in dominant wind directions themselves, yet other locations do not exhibit any significant wind condition changes in late-century. In this section, we investigate the robustness of such projected changes by considering the uncertainty due to internal variability (Wang et al, 2018) that is caused by a perturbation in initial conditions. We calculate the commonly used internal variability as well as the newly defined internal variability for standard deviation (left panels) and 95th-quantile (right panels) following Sect.…”

Section: Projected Climate Change Versus Internal Variabilitymentioning

confidence: 99%

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Station-wise statistical joint assessment of wind speed and direction under future climates across the United States

Wu¹,

Bessac²,

Huang³

et al. 2022

Preprint

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This study develops a statistical conditional approach to evaluate climate model performance in wind speed and direction and to project their future changes under the representative concentration pathway (RCP) 8.5 scenario over inland and offshore locations across the Continental United States (CONUS). The proposed conditional approach extends the scope of existing studies by characterizing the changes of the full range of the joint wind speed and direction distribution. A von Mises mixture distribution is used to model wind directions across models and climate conditions. Directional wind speed distributions are estimated using two statistical methods: a Weibull distributional regression model and a quantile regression model, both of which enforce the circular constraint to their resulting estimates of directional distributions. Projected uncertainties associated with different climate models and model internal variability are investigated and compared with the climate change signal to quantify the statistical significance of the future projections. In particular, this work extends the concept of internal variability to the standard deviation and high quantiles to assess the relative magnitudes to their projected changes. The evaluation results show that the studied climate model capture both historical wind speed, wind direction, and their dependencies reasonably well over both inland and offshore locations. In the future, most of the locations show no significant changes in mean wind speeds in both winter and summer, although the changes in standard deviation and 95thquantile show some robust changes over certain locations in winter. However, in winter, high directional wind speeds are projected to decrease in Northwest, Colorado and Northern Great Plains. In summer, directional high wind speeds over southern Great Plains slightly increase while high directional wind speeds over offshore locations do not change in future. The proposed conditional approach enables the characterization of the directional wind speed distributions, which offers additional insights for the joint assessment of speed and direction.

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How essential of the balance between large and small scale features to reproduce precipitation during a sudden sharp turn from drought to flood

Yang

Wang

2018

Clim Dyn

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Internal variability of a dynamically downscaled climate over North America

Cited by 6 publications

References 23 publications

Impacts of Lake Surface Temperature on the Summer Climate Over the Great Lakes Region

Impacts of Lake Surface Temperature on the Summer Climate Over the Great Lakes Region

Station-wise statistical joint assessment of wind speed and direction under future climates across the United States

How essential of the balance between large and small scale features to reproduce precipitation during a sudden sharp turn from drought to flood

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